Principal Investigator

Evangelos K. Oikonomou, MD, DPhil, is a cardiologist and physician-scientist, and an Assistant Professor of Medicine in the Section of Cardiovascular Medicine at Yale School of Medicine. His work focuses on artificial intelligence-enabled digital biomarkers, with an emphasis on computer vision for cardiovascular phenotyping. His research aims to build practical tools for earlier diagnosis, sharper risk stratification, and better treatment decisions in routine care.

Dr. Oikonomou graduated as valedictorian from the National and Kapodistrian University of Athens School of Medicine and earned his doctorate (DPhil) in Medical Sciences from the University of Oxford. He subsequently completed the Yale Physician-Scientist Training Program, including residency in Internal Medicine and fellowship training in Cardiovascular Medicine. His research has been supported by a Ruth L. Kirschstein National Research Service Award (F32, 2023-2025) from the National Heart, Lung, and Blood Institute, a Robert A. Winn Excellence in Clinical Trials Program (2025-2028), and an American Heart Association Career Development Award (2026-2029).

He has received multiple national and international honors, including Young Investigator Awards from the American Heart Association (2021, 2023), American College of Cardiology (2024), European Society of Cardiology (2018, 2019), and the Society of Cardiovascular Computed Tomography (2017), as well as the American Society for Clinical Investigation Emerging Generation (E-Gen) Award (2024) and the Wiesman Award from the ATTR Early-Career Research Forum (2025).

His interdisciplinary research lies at the intersection of cardiovascular and cardiometabolic medicine and focuses on four major areas: (i) the development and clinical translation of adipose tissue imaging biomarkers to elucidate the early links between adiposity and cardiovascular disease; (ii) the design and validation of deep learning algorithms for point-of-care echocardiography to detect both common and under-recognized cardiomyopathies; (iii) the data-driven evaluation of treatment-effect heterogeneity in clinical trials to inform adaptive and precision-enriched trial design; and (iv) the multimodal integration of these approaches into clinical care pathways through innovative clinical informatics approaches. His work has been published in The Lancet, The Lancet Digital Health, JAMA, JAMA Cardiology, European Heart Journal, JACC, Circulation, and Diabetes Care, among others.

His current work uses multimodal AI to study subclinical disease detection, longitudinal risk prediction, clinical implementation, and equitable access to advanced diagnostics.